Optimizing Demonstrated Robot Manipulation Skills for Temporal Logic Constraints

For performing robotic manipulation tasks, the core problem is determining suitable trajectories that fulfill the task requirements. Various approaches to compute such trajectories exist, being learning and optimization the main driving techniques. Our work builds on the learning-from-demonstration (LfD) paradigm, where an expert demonstrates motions, and the robot learns to imitate them. However, expert demonstrations are not sufficient to capture all sorts of task specifications, such as the timing to grasp an object. In this paper, we propose a new method that considers formal task specifications within LfD skills. Precisely, we leverage Signal Temporal Logic (STL), an expressive form of temporal properties of systems, to formulate task specifications and use black-box optimization (BBO) to adapt an LfD skill accordingly. We demonstrate our approach in simulation and on a real industrial setting using several tasks that showcase how our approach addresses the LfD limitations using STL and BBO.Comment: 8 Pages, 11 Figures. Accepted at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022

Integrating Economic and Ecological Benchmarking for a Sustainable Development of Hydropower

Hydropower reservoirs play an increasingly important role for the global electricity supply. Reservoirs are anthropogenically-dominated ecosystems because hydropower operations induce artificial water level fluctuations (WLF) that exceed natural fluctuations in frequency and amplitude. These WLF have detrimental ecological effects, which can be quantified as losses to ecosystem primary production due to lake bottoms that fall dry. To allow for a sustainable development of hydropower, these “ecological costs” of WLF need to be weighed against the “economic benefits” of hydropower that can balance and store intermittent renewable energy. We designed an economic hydropower operation model to derive WLF in large and small reservoirs for three different future energy market scenarios and quantified the according losses in ecosystem primary production in semi-natural outdoor experiments. Our results show that variations in market conditions affect WLF differently in small and large hydropower reservoirs and that increasing price volatility magnified WLF and reduced primary production. Our model allows an assessment of the trade-off between the objectives of preserving environmental resources and economic development, which lies at the core of emerging sustainability issues

The e-Bike Motor Assembly: Towards Advanced Robotic Manipulation for Flexible Manufacturing

Robotic manipulation is currently undergoing a profound paradigm shift due to the increasing needs for flexible manufacturing systems, and at the same time, because of the advances in enabling technologies such as sensing, learning, optimization, and hardware. This demands for robots that can observe and reason about their workspace, and that are skillfull enough to complete various assembly processes in weakly-structured settings. Moreover, it remains a great challenge to enable operators for teaching robots on-site, while managing the inherent complexity of perception, control, motion planning and reaction to unexpected situations. Motivated by real-world industrial applications, this paper demonstrates the potential of such a paradigm shift in robotics on the industrial case of an e-Bike motor assembly. The paper presents a concept for teaching and programming adaptive robots on-site and demonstrates their potential for the named applications. The framework includes: (i) a method to teach perception systems onsite in a self-supervised manner, (ii) a general representation of object-centric motion skills and force-sensitive assembly skills, both learned from demonstration, (iii) a sequencing approach that exploits a human-designed plan to perform complex tasks, and (iv) a system solution for adapting and optimizing skills online. The aforementioned components are interfaced through a four-layer software architecture that makes our framework a tangible industrial technology. To demonstrate the generality of the proposed framework, we provide, in addition to the motivating e-Bike motor assembly, a further case study on dense box packing for logistics automation

Specification Decomposition and Formal Behavior Generation in Multi-Robot Systems

Autonomous robot systems are becoming increasingly common in service applications and industrial scenarios. However, their use is still mostly limited to rather simple tasks. This primarily results from the considerable effort that is required to manually program the execution plans of the robots. In this thesis, we discuss how the behavior of robots can be automatically generated from a given goal specification. This forms the basis for providing formal guarantees regarding optimality and satisfaction of the mission goal specification and creates the opportunity to deploy these robots in increasingly sophisticated scenarios. Well-defined robot capabilities of comparably low complexity can be developed independently from a specific high-level goal and a behavior planner can then automatically compose them to achieve complex goals in a verifiably correct way. Intelligent coordination of a robot team can highly improve the performance of a system, but at the same time, considering multiple robots introduces significant additional planning complexity. To address the complexity, a framework is proposed to efficiently plan actions for multi-robot systems. The generated behavior of the robots is guaranteed to fulfill complex, temporally extended, formal mission specifications posed to the team as a whole. To achieve this, several challenges are addressed such as decomposition of a specification into tasks, allocation of tasks to robots, planning of actions to execute the assigned tasks, and coordination of action execution. This enables the combination of heterogeneous robots for automating tasks in a wide range of practically relevant applications. The proposed methods determine efficient actions for each robot in the sense that these actions are optimal in the absence of execution uncertainty and otherwise, improve the solution performance over time based on online observations. First, to plan optimal actions, an approach called Simultaneous Task Allocation and Planning is proposed to utilize the interplay of allocation and planning based on automatically identified, independently executable tasks. Second, to improve performance in the presence of stochastic actions, a Hierarchical LTL-Task MDP is proposed to combine auction-based allocation with reinforcement learning to achieve the desired performance with feasible computational effort. Both approaches of the presented framework are evaluated in the considered use case areas of service robotics and factory automation. The results of this thesis enable to plan correct-by-construction behavior from expressive specifications in more complex and realistic scenarios than possible with previous approaches.Även om autonoma robotsystem blir allt vanligare är deras användning fortfarande mestadels begränsad till ganska enkla uppgifter. Detta beror främst på att manuell programmering av robotarnas exekveringsplaner behövs. Istället, som det visas i denna avhandling, kan deras beteende genereras automatiskt från en given målspecifikation. Detta utgör fundamentet för att ge en formell garanti att det resulterande beteendet är optimalt och uppdragsmålspecifikationen är uppfylld. Därför skapar det möjlighet att använda dessa robotar i alltmer sofistikerade scenarier. Väldefinierade robotkompetenser med relativt låg komplexitet kan utvecklas oberoende av ett specifikt mål på hög nivå och sedan sammansättas automatiskt med hjälp av en beteendeplanerare för att uppnå komplexa mål på ett verifierbar korrekt sätt. Om det handlar om flera robotar så introduceras ytterligare planeringskomplexitet som är betydande. Inte bara åtgärder behöver planeras, men även fördelning av uppdragets olika delar till de enskilda robotarna måste hanteras. Traditionellt anses planering och allokering som två oberoende problem som kräver att man löser ett exponentiellt antal planeringsproblem, eller så leder formuleringen av en gemensam modell för hela gruppen till ett produkttillståndsutrymme mellan robotarna. Den resulterande exponentiella komplexiteten förhindrar att de flesta befintliga metoderna är praktiskt användbara i mer komplexa och realistiska scenarier. I denna avhandling presenteras ett tillvägagångssätt för att utnyttja samspelet mellan allokering och planering, som undviker exponentiell komplexitet för oberoende exekverbara delar av uppdragsspecifikationen. Dessutom presenteras ett tillvägagångssätt för att automatiskt identifiera dessa oberoende delar när endast en enda målspecifikation ges för arbetslaget. Detta har potential att förbättra effektiviteten för att hitta en optimal lösning och är ett viktigt steg mot tillämpningen av formell multi-robot-beteendeplanering för realistiska problem. Effektiviteten av de föreslagna metoderna illustreras därför i experiment baserade på en befintlig kontorsmiljö och i realistiska scenarier.Not duplicate with DiVA 1145120QC 20190411</p

An Approach for Runtime-Modifiable Behavior Control of Humanoid Rescue Robots

This thesis describes a novel approach for modification of robot behaviors during runtime. Existing high-level robot control has very limited adaptability regarding unexpected disturbances. Possible uncertainties have to be known and explicitly considered in advance by defining strategies of how to react to these. This requirement confines the development of robust robots as for example required in complex and unstructured disaster mitigation scenarios. In order to overcome this limitation and to facilitate the development of more flexible high-level robot behavior control, the approach developed in this thesis enables to change the whole structure of behaviors even while they are executed. Therefore, the operator is able to incorporate situational knowledge gained during execution, and thus to compensate insufficient a priori knowledge about the status of the environment. The approach is proposed based on modeling behaviors as hierarchical state machines, allowing for modular composition and intuitive specification in different levels of abstraction. Detailed monitoring of the state of execution and occurred errors assists the operator when giving commands and adjusting the level of autonomy, utilizing the individual capabilities of both robot and operator in a cooperative manner. Nevertheless, the developed framework is able to cope with severe restrictions on the communication channel to the robot and is robust regarding runtime failure. In addition, verification of specified behaviors greatly reduces the risk of failure. As part of this thesis, the behavior engine FlexBE has been developed in order to implement and refine the promoted concepts. It comes with a comprehensive user interface and behavior editor and is practically applied in the upcoming DARPA Robotics Challenge Finals

Specification Decomposition and Formal Behavior Generation in Multi-Robot Systems

While autonomous robot systems are becoming increasingly common, their usage is still mostly limited to rather simple tasks. This primarily results from the need for manually programming the execution plans of the robots. Instead, as shown in this thesis, their behavior can be automatically generated from a given goal specification. This forms the basis for providing formal guarantees regarding optimality and satisfaction of the mission goal specification and creates the opportunity to deploy these robots in increasingly sophisticated scenarios. Well-defined robot capabilities of comparably low complexity can be developed independently from a specific high-level goal and then, using a behavior planner, be automatically composed to achieve complex goals in a verifiably correct way. Considering multiple robots introduces significant additional planning complexity. Not only actions need to be planned, but also allocation of parts of the mission to the individual robots needs to be considered. Classically, either are planning and allocation seen as two independent problems which requires to solve an exponential number of planning problems, or the formulation of a joint team model leads to a product state space between the robots. The resulting exponential complexity prevents most existing approaches from being practically useful in more complex and realistic scenarios. In this thesis, an approach is presented to utilize the interplay of allocation and planning, which avoids the exponential complexity for independently executable parts of the mission specification. Furthermore, an approach is presented to identify these independent parts automatically when only being given a single goal specification for the team. This bears the potential of improving the efficiency to find an optimal solution and is a significant step towards the application of formal multi-robot behavior planning to real-world problems. The effectiveness of the proposed methods is therefore illustrated in experiments based on an existing office environment and in realistic scenarios.Även om autonoma robotsystem blir allt vanligare är deras användning fortfarande mestadels begränsad till ganska enkla uppgifter. Detta beror främst på att manuell programmering av robotarnas exekveringsplaner behövs. Istället, som det visas i denna avhandling, kan deras beteende genereras automatiskt från en given målspecifikation. Detta utgör fundamentet för att ge en formell garanti att det resulterande beteendet är optimalt och uppdragsmålspecifikationen är uppfylld. Därför skapar det möjlighet att använda dessa robotar i alltmer sofistikerade scenarier. Väldefinierade robotkompetenser med relativt låg komplexitet kan utvecklas oberoende av ett specifikt mål på hög nivå och sedan sammansättas automatiskt med hjälp av en beteendeplanerare för att uppnå komplexa mål på ett verifierbar korrekt sätt. Om det handlar om flera robotar så introduceras ytterligare planeringskomplexitet som är betydande. Inte bara åtgärder behöver planeras, men även fördelning av uppdragets olika delar till de enskilda robotarna måste hanteras. Traditionellt anses planering och allokering som två oberoende problem som kräver att man löser ett exponentiellt antal planeringsproblem, eller så leder formuleringen av en gemensam modell för hela gruppen till ett produkttillståndsutrymme mellan robotarna. Den resulterande exponentiella komplexiteten förhindrar att de flesta befintliga metoderna är praktiskt användbara i mer komplexa och realistiska scenarier. I denna avhandling presenteras ett tillvägagångssätt för att utnyttja samspelet mellan allokering och planering, som undviker exponentiell komplexitet för oberoende exekverbara delar av uppdragsspecifikationen. Dessutom presenteras ett tillvägagångssätt för att automatiskt identifiera dessa oberoende delar när endast en enda målspecifikation ges för arbetslaget. Detta har potential att förbättra effektiviteten för att hitta en optimal lösning och är ett viktigt steg mot tillämpningen av formell multi-robot-beteendeplanering för realistiska problem. Effektiviteten av de föreslagna metoderna illustreras därför i experiment baserade på en befintlig kontorsmiljö och i realistiska scenarier.QC 20170928</p

Specification Decomposition and Formal Behavior Generation in Multi-Robot Systems

Autonomous robot systems are becoming increasingly common in service applications and industrial scenarios. However, their use is still mostly limited to rather simple tasks. This primarily results from the considerable effort that is required to manually program the execution plans of the robots. In this thesis, we discuss how the behavior of robots can be automatically generated from a given goal specification. This forms the basis for providing formal guarantees regarding optimality and satisfaction of the mission goal specification and creates the opportunity to deploy these robots in increasingly sophisticated scenarios. Well-defined robot capabilities of comparably low complexity can be developed independently from a specific high-level goal and a behavior planner can then automatically compose them to achieve complex goals in a verifiably correct way. Intelligent coordination of a robot team can highly improve the performance of a system, but at the same time, considering multiple robots introduces significant additional planning complexity. To address the complexity, a framework is proposed to efficiently plan actions for multi-robot systems. The generated behavior of the robots is guaranteed to fulfill complex, temporally extended, formal mission specifications posed to the team as a whole. To achieve this, several challenges are addressed such as decomposition of a specification into tasks, allocation of tasks to robots, planning of actions to execute the assigned tasks, and coordination of action execution. This enables the combination of heterogeneous robots for automating tasks in a wide range of practically relevant applications. The proposed methods determine efficient actions for each robot in the sense that these actions are optimal in the absence of execution uncertainty and otherwise, improve the solution performance over time based on online observations. First, to plan optimal actions, an approach called Simultaneous Task Allocation and Planning is proposed to utilize the interplay of allocation and planning based on automatically identified, independently executable tasks. Second, to improve performance in the presence of stochastic actions, a Hierarchical LTL-Task MDP is proposed to combine auction-based allocation with reinforcement learning to achieve the desired performance with feasible computational effort. Both approaches of the presented framework are evaluated in the considered use case areas of service robotics and factory automation. The results of this thesis enable to plan correct-by-construction behavior from expressive specifications in more complex and realistic scenarios than possible with previous approaches.Även om autonoma robotsystem blir allt vanligare är deras användning fortfarande mestadels begränsad till ganska enkla uppgifter. Detta beror främst på att manuell programmering av robotarnas exekveringsplaner behövs. Istället, som det visas i denna avhandling, kan deras beteende genereras automatiskt från en given målspecifikation. Detta utgör fundamentet för att ge en formell garanti att det resulterande beteendet är optimalt och uppdragsmålspecifikationen är uppfylld. Därför skapar det möjlighet att använda dessa robotar i alltmer sofistikerade scenarier. Väldefinierade robotkompetenser med relativt låg komplexitet kan utvecklas oberoende av ett specifikt mål på hög nivå och sedan sammansättas automatiskt med hjälp av en beteendeplanerare för att uppnå komplexa mål på ett verifierbar korrekt sätt. Om det handlar om flera robotar så introduceras ytterligare planeringskomplexitet som är betydande. Inte bara åtgärder behöver planeras, men även fördelning av uppdragets olika delar till de enskilda robotarna måste hanteras. Traditionellt anses planering och allokering som två oberoende problem som kräver att man löser ett exponentiellt antal planeringsproblem, eller så leder formuleringen av en gemensam modell för hela gruppen till ett produkttillståndsutrymme mellan robotarna. Den resulterande exponentiella komplexiteten förhindrar att de flesta befintliga metoderna är praktiskt användbara i mer komplexa och realistiska scenarier. I denna avhandling presenteras ett tillvägagångssätt för att utnyttja samspelet mellan allokering och planering, som undviker exponentiell komplexitet för oberoende exekverbara delar av uppdragsspecifikationen. Dessutom presenteras ett tillvägagångssätt för att automatiskt identifiera dessa oberoende delar när endast en enda målspecifikation ges för arbetslaget. Detta har potential att förbättra effektiviteten för att hitta en optimal lösning och är ett viktigt steg mot tillämpningen av formell multi-robot-beteendeplanering för realistiska problem. Effektiviteten av de föreslagna metoderna illustreras därför i experiment baserade på en befintlig kontorsmiljö och i realistiska scenarier.QC 20190411</p

Specification Decomposition and Formal Behavior Generation in Multi-Robot Systems

Autonomous robot systems are becoming increasingly common in service applications and industrial scenarios. However, their use is still mostly limited to rather simple tasks. This primarily results from the considerable effort that is required to manually program the execution plans of the robots. In this thesis, we discuss how the behavior of robots can be automatically generated from a given goal specification. This forms the basis for providing formal guarantees regarding optimality and satisfaction of the mission goal specification and creates the opportunity to deploy these robots in increasingly sophisticated scenarios. Well-defined robot capabilities of comparably low complexity can be developed independently from a specific high-level goal and a behavior planner can then automatically compose them to achieve complex goals in a verifiably correct way. Intelligent coordination of a robot team can highly improve the performance of a system, but at the same time, considering multiple robots introduces significant additional planning complexity. To address the complexity, a framework is proposed to efficiently plan actions for multi-robot systems. The generated behavior of the robots is guaranteed to fulfill complex, temporally extended, formal mission specifications posed to the team as a whole. To achieve this, several challenges are addressed such as decomposition of a specification into tasks, allocation of tasks to robots, planning of actions to execute the assigned tasks, and coordination of action execution. This enables the combination of heterogeneous robots for automating tasks in a wide range of practically relevant applications. The proposed methods determine efficient actions for each robot in the sense that these actions are optimal in the absence of execution uncertainty and otherwise, improve the solution performance over time based on online observations. First, to plan optimal actions, an approach called Simultaneous Task Allocation and Planning is proposed to utilize the interplay of allocation and planning based on automatically identified, independently executable tasks. Second, to improve performance in the presence of stochastic actions, a Hierarchical LTL-Task MDP is proposed to combine auction-based allocation with reinforcement learning to achieve the desired performance with feasible computational effort. Both approaches of the presented framework are evaluated in the considered use case areas of service robotics and factory automation. The results of this thesis enable to plan correct-by-construction behavior from expressive specifications in more complex and realistic scenarios than possible with previous approaches.Även om autonoma robotsystem blir allt vanligare är deras användning fortfarande mestadels begränsad till ganska enkla uppgifter. Detta beror främst på att manuell programmering av robotarnas exekveringsplaner behövs. Istället, som det visas i denna avhandling, kan deras beteende genereras automatiskt från en given målspecifikation. Detta utgör fundamentet för att ge en formell garanti att det resulterande beteendet är optimalt och uppdragsmålspecifikationen är uppfylld. Därför skapar det möjlighet att använda dessa robotar i alltmer sofistikerade scenarier. Väldefinierade robotkompetenser med relativt låg komplexitet kan utvecklas oberoende av ett specifikt mål på hög nivå och sedan sammansättas automatiskt med hjälp av en beteendeplanerare för att uppnå komplexa mål på ett verifierbar korrekt sätt. Om det handlar om flera robotar så introduceras ytterligare planeringskomplexitet som är betydande. Inte bara åtgärder behöver planeras, men även fördelning av uppdragets olika delar till de enskilda robotarna måste hanteras. Traditionellt anses planering och allokering som två oberoende problem som kräver att man löser ett exponentiellt antal planeringsproblem, eller så leder formuleringen av en gemensam modell för hela gruppen till ett produkttillståndsutrymme mellan robotarna. Den resulterande exponentiella komplexiteten förhindrar att de flesta befintliga metoderna är praktiskt användbara i mer komplexa och realistiska scenarier. I denna avhandling presenteras ett tillvägagångssätt för att utnyttja samspelet mellan allokering och planering, som undviker exponentiell komplexitet för oberoende exekverbara delar av uppdragsspecifikationen. Dessutom presenteras ett tillvägagångssätt för att automatiskt identifiera dessa oberoende delar när endast en enda målspecifikation ges för arbetslaget. Detta har potential att förbättra effektiviteten för att hitta en optimal lösning och är ett viktigt steg mot tillämpningen av formell multi-robot-beteendeplanering för realistiska problem. Effektiviteten av de föreslagna metoderna illustreras därför i experiment baserade på en befintlig kontorsmiljö och i realistiska scenarier.Not duplicate with DiVA 1145120QC 20190411</p